Microchip Based Isolation and Drug Delivery of Patient-Derived Extracellular Vesicles Against Their Homologous Tumor.

Extracellular vesicles (EVs) have demonstrated significant potential in drug delivery and anti-tumor therapy. Despite this promising strategy, challenges such as specific targeting, EVs purification persist. In this study, a personalized nanodrug delivery platform using patient-derived tumor EVs (PT-EVs) based on a microchip is presented. The microchip integrates multiple functions, including capture, enrichment, drug loading, and elution of PT-EVs. The isolation and drug-carrying procedures are completed within a 12 h timeframe, achieving a recovery rate of 65%, significantly surpassing the conventional ultracentrifuge (UC) method. Furthermore, PT-EVs derived from patient tumor models are first utilized as natural drug carriers, capitalizing on their inherent homing ability to precisely target homologous tumors. Lenvatinib and doxorubicin (DOX), two commonly utilized drugs in the clinical treatment of hepatocellular carcinoma (HCC), are loaded into PT-EVs and delivered to a matched in vitro tumor model that recapitulates original tumors for drug susceptibility testing. As is proven, PT-EVs exhibit robust tumor cell targeting and efficient receptor-mediated cellular uptake, and the efficacy of chemotherapeutic drugs is improved significantly. These results suggest that this platform could be a valuable tool for efficient isolation of PT-EVs and personalized drug customization, particularly when working with limited clinical samples, thus supporting personalized and precision medicine.

Comprehensive Detection of PD-L1 Protein and mRNA in Tumor Cells and Extracellular Vesicles through a Real-Time qPCR Assay.

A growing number of studies have shown that tumor cells secrete extracellular vesicles (EVs) containing programmed death-ligand 1 (PD-L1) protein. These vesicles can travel to lymph nodes and remotely inactivate T cells, thereby evading immune system attack. Therefore, the simultaneous detection of PD-L1 protein expression in cells and EVs is of great significance in guiding immunotherapy. Herein, we developed a method based on qPCR for the simultaneous detection of PD-L1 protein and mRNA in EVs and their parental cells (PREC-qPCR assay). Lipid probes immobilized on magnetic beads were used to capture EVs directly from samples. For RNA assay, EVs were directly broken by heating and quantified with qPCR. As to protein assay, EVs were recognized and bound with specific probes (such as aptamers), which were used as templates in subsequent qPCR analysis. This method was used to analyze EVs of patient-derived tumor clusters (PTCs) and plasma samples from patients and healthy volunteers. The results revealed that the expression of exosomal PD-L1 in PTCs was correlated with tumor types and significantly higher in plasma-derived EVs from tumor patients than that of healthy individuals. When extended to cells and PD-L1 mRNAs, the results showed that the expression of PD-L1 protein was consistent with mRNA in cancer cell lines, while PTCs demonstrated significant heterogeneity. This comprehensive detection of PD-L1 at four levels (cell, EVs, protein, and mRNA) is believed to enhance our understanding of the relationship among PD-L1, tumors, and the immune system and to provide a promising tool for predicting the benefits of immunotherapy.

Assessment of Drug Susceptibility for Patient-Derived Tumor Models through Lactate Biosensing and Machine Learning.

A patient-derived tumor model (PDM) is a practical tool to rapidly screen chemotherapeutics for individual patients. The evaluation method of cell viability directly determines the application of PDMs for drug susceptibility testing. As one of the metabolites of "glycosis", the lactate content was used to evaluate cell viability, but these assays were not specific for tumor cells. Based on the "Warburg effect", wherein tumor cells preferentially rely on "aerobic glycolysis" to produce lactate instead of pyruvate in "anaerobic glycolysis" of normal cells, we reported a gold lactate sensor (GLS) to estimate the cell viability of PDMs in drug susceptibility testing. It demonstrated high consistency between the GLS and commercial cell viability assay. Unlike either imaging or cell viability assay, the GLS characterizes the cell viability, enables dynamic monitoring, and distinguishes tumor cells from other cells. Moreover, machine learning (ML) was employed to perform a multi-index assessment for drug susceptibility of PDMs, which proved to be accurate and practical for clinical application. Therefore, the GLS provides an ideal drug susceptibility testing tool for individualized medicine.

Rapid enrichment and sensitive detection of extracellular vesicles through measuring the phospholipids and transmembrane protein in a microfluidic chip.

Extracellular vesicles (EVs) have attracted tremendous attention in recent years and quantification of EVs is a key issue in the evaluation of vesicle-based diagnostics and therapeutic development, but it's quite challenging to determine whether higher protein expression signals are due to larger vesicle amount or higher protein content within each vesicle. To solve this problem, herein, we proposed a strategy based on staining phospholipid bilayers of EVs with lipophilic dyes to evaluate their lipid amount, which was subsequently normalized as an internal standard for studying the expression of transmembrane protein (i.e., CD63) on EVs in different samples. In addition, a microfluidic platform based on electrophoresis technology was invented to effectively enrich and detect EVs. Small fluorescent labeling molecules (i.e., uncombined aptamers) were on-chip removed from EVs without pre-separation via ultracentrifugation or ultrafiltration which were indispensable in nanoparticle tracking analysis (NTA) and flow cytometry techniques and the performance of this assay is comparable to NTA. Finally, it was found obvious difference in the expression of CD63 on EVs before and after normalization based on lipid amount in plasma samples. This method is expected to provide more accurate information when comparing the expression levels of EVs biomarkers in different samples.